Many parts of a wireless communications receiver often require an estimation of signal to interference ratio (SIR), signal to-noise ratio (SNR), or (more generically to include SIR and/or SNR) noise plus interference ratio (SNIR). This is needed for purposes of power control, threshold determination for various algorithms, quantisation of soft-decision information for channel decoding purposes to name but a few.
A well-known SNIR estimation technique derives its estimated SNIR {circumflex over (Z)} as
      Z    ^    =                    [                  E          ⁢                      {                                                        r                ⁡                                  (                  t                  )                                                                    }                          ]            2                      E        ⁢                  {                                    r              2                        ⁡                          (              t              )                                }                    -                        [                      E            ⁢                          {                                                                r                  ⁡                                      (                    t                    )                                                                              }                                ]                2            where E represents mean value and r(t) represents the combination of signal s(t) and noise n(t).
However, this known estimator suffers from a bias term under conditions of low signal to noise ratio.
A need therefore exists for SNIR estimation wherein the abovementioned disadvantages may be alleviated.